Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

Pandey, Daya S. and Das, Saptarshi and Pan, Indranil and Leahy, James J. and Kwapinski, Witold (2016) Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor. Waste Management, 58. pp. 202-213. ISSN 1879-2456

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Official URL: http://dx.doi.org/10.1016/j.wasman.2016.08.023

Abstract

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg–Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier.

Item Type: Journal Article
Keywords: Municipal solid waste, Gasification, Artificial neural networks, Feed-forward multilayer perceptron, Fluidized bed gasifier
Faculty: Faculty of Science & Technology
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 02 Apr 2019 09:09
Last Modified: 16 Jul 2019 09:39
URI: http://arro.anglia.ac.uk/id/eprint/704224

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